• Energy Research

Abstract The Korean government has given efforts to reduce green-house gas emissions. In order to avoid the monolithic system that only focuses on reducing the emission without considering operational environments of firms, the government should encourage respective firms to improve their energy efficiency which wastes less energy but produces maximum economic revenue. This paper employed a DEA (Data Envelopment Analysis) model to measure energy efficiency of energy intensive manufacturing firms in Korea. Identifying and comparing the difference in efficiency score by using the simplified single-stage DEA model is not sufficient to give governments and firms meaningful policy implications related to energy efficiency improvement. To compensate for this insufficiency, this study extended the single-stage model into a two-stage model that includes two efficiency measures: pure energy efficiency which is only for energy-related process and economy efficiency which is for the pursuit of profit, to reach the improvement of total energy efficiency. By using the two-stage DEA model, it has identified that the difference from overall energy efficiency is not caused by pure energy efficiency but rather by economy efficiency. In addition, this paper statistically analyzed the effects of environmental variables such as firm size, possession of certified management systems and emission type.

In recent years, big data has been widely used to understand consumers’ behavior and opinions. With this paper, we consider the use of big data and its effects in the problem of projecting the number of reverse mortgage subscribers in Korea. We analyzed web-news, blog post, and search traffic volumes associated with Korean reverse mortgages and integrated them into a Generalized Bass Model (GBM) as a part of the exogenous variables representing marketing effort. We particularly consider web-news volume as a proxy for marketer-generated content (MGC) and blog post and search traffic volumes as proxies for user-generated content (UGC). Empirical analysis provides some interesting findings: First, the GBM by incorporating big data is helpful for forecasting the sales of Korean reverse mortgages, and second, the UGC as an exogenous variable is more useful for predicting sales volume than the MGC. The UGC can explain consumers’ interest relatively well. Additional sensitivity analysis supports that the UGC is important for increasing sales volume. Finally, prediction performance is different between blog posts and search traffic volumes.

This study identifies the optimal management policy of a given energy storage system (ESS) installed in a grid-connected wind farm in terms of maximizing the monetary benefits and provides guidelines for defining the economic value of the ESS under optimal management policy and selecting the optimal size of the ESS based on economic value. Considering stochastic models for wind power and electricity price, we develop a finite-horizon periodic-review Markov decision process (MDP) model to seek the optimal management policy. We also use a simple optimization model to find the optimal storage capacity and charging/discharging capacity of the ESS. By applying our analytic approach to a real-world grid-connected wind farm located in South Korea, we verify the usefulness of this study. Our numerical study shows that the economic value of the ESS is highly dependent on management policy, wind electricity variability, and electricity price variability. Thus, the optimal size of ESS should be carefully determined based on the locational characteristics and management policy even with limited investments. Furthermore, this study provides a meaningful policy implication regarding how much of a subsidy the government should provide for installing ESS in a wind farm.

Abstract This paper presents a practical portfolio model for the long-term power generation mix problem. The proposed model optimizes the power generation mix by striking a trade-off between the expected cost of power generation and its variability. We use Monte Carlo simulation techniques to consider the uncertainty associated with future electricity demand, fuel prices and their correlations, and the capital costs of power plants. Unlike in the case of conventional power generation mix models, we employ CVaR (Conditional Value-at-Risk) in designing variability to consider events that are rare but enormously expensive. A comprehensive analysis on South Korea's generation policy using the portfolio model shows that a large annual cost is additionally charged to substitute a portion of nuclear energy with other alternatives. Nonetheless, if Korea has to reduce its dependency on nuclear energy because of undermined social receptivity from the Fukushima disaster, it turns out that LNG or coal could be a secure candidate from an economic perspective.

Recently, much research has focused on lowering carbon emissions in logistics. This paper attempts to contribute to the literature on the joint shipment size and carbon reduction decisions by developing novel models for distribution systems under direct shipment and peddling distribution strategies. Unlike the literature that has simply investigated the effects of carbon costs on operational decisions, we address how to reduce carbon emissions and logistics costs by adjusting shipment size and making an optimal decision on carbon reduction investment. An optimal decision is made by analyzing the distribution cost including not only logistics and carbon trading costs but also the cost for adjusting carbon emission factors. No research has explicitly considered the two sources of carbon emissions, but we develop a model covering the difference in managing carbon emissions from transportation and storage. Structural analysis guides how to determine an optimal shipment size and emission factors in a closed form. Moreover, we analytically prove the possibility of reducing the distribution cost and carbon emissions at the same time. Numerical analysis follows validation of the results and demonstrates some interesting findings on carbon and distribution cost reduction.